Comparative life cycle assessment of on-site sanitation systems using lagoons or drying beds for fecal sludge treatment in low-income tropical countries
Bibliographic record
Abstract
ABSTRACT Environmental challenges in low-income countries, such as Haiti, persist due to inadequate sanitation infrastructure. This study assesses the environmental impacts of nine on-site sanitation systems to identify those with the least environmental impacts and explore improvement options. Nine scenarios were developed, each representing different systems for managing 1 ton of fecal sludge over 1 year. The ‘Impact World + ’ and ‘IPCC 2013 GWP 100a’ methods evaluated impacts on ecosystems, human health, and climate change. Data sources included interviews, weighing records, and scientific publications. Results show that Scenario 8 (Flush Toilet – Evacuation – Planted Drying Beds) is most impactful on health (1.17 × 10−2 DALY), while Scenario 1 (Composting Toilet – Evacuation – Unplanted Drying Beds) is least impactful (1.77 × 10−3 DALY). For ecosystem impacts, Scenario 2 (Container-based Toilet – Evacuation – Planted Drying Beds) is most impactful (3.81 × 103 PDF·m2·year), while Scenario 6 (VIP latrine – Evacuation – Lagoons) is least impactful (3.52 × 103 PDF·m2·year). Key hotspots include toilet paper, wood shavings, GHG emissions, and water use. The study recommends an integrated approach combining environmental life cycle assessment (LCA) with life cycle cost assessment and social LCA for sustainable decision-making on sanitation systems in low-income countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".